Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: obtaining, by a device, simulation environment data regarding traffic generated within a simulation environment in which malware is executed; training, by the device, a malware detector using the simulation environment data; obtaining, by the device, deployment environment characteristics of a network to which the malware detector is to be deployed; training, by the device, a first machine learning-based classifier to distinguish between traffic in the simulation environment in which malware is executed and traffic in a deployment environment in which the malware detector is to be deployed; and configuring, by the device, the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics using the trained first machine learning-based classifier.
This invention relates to improving malware detection in network environments by adapting simulation-based training to real-world deployment conditions. The problem addressed is the mismatch between simulated malware traffic and actual network traffic, which can reduce the effectiveness of malware detectors trained in simulation environments. The method involves obtaining simulation environment data from a controlled environment where malware is executed, then training a malware detector using this data. To bridge the gap between simulation and deployment, the method further obtains characteristics of the target network environment where the malware detector will be deployed. A machine learning-based classifier is trained to distinguish between traffic patterns in the simulation environment and those in the deployment environment. The malware detector is then configured to ignore simulation data associated with environment characteristics not present in the deployment environment, ensuring the detector focuses on relevant traffic patterns. This approach enhances detection accuracy by filtering out irrelevant simulation artifacts, making the detector more effective in real-world scenarios. The method leverages machine learning to adapt simulation-trained models to specific network conditions, improving reliability and reducing false positives.
2. The method as in claim 1 , wherein the one or more environment characteristics comprise at least one of: an endpoint operating system, a Transport Layer Security (TLS) library, or a Hypertext Transfer Protocol (HTTP) User-Agent.
This invention relates to cybersecurity, specifically to methods for detecting and mitigating threats in networked environments by analyzing environment characteristics. The problem addressed is the difficulty in accurately identifying and responding to threats due to variations in endpoint configurations, software versions, and communication protocols, which can affect threat detection accuracy. The method involves collecting and analyzing one or more environment characteristics to enhance threat detection. These characteristics include the endpoint operating system, the Transport Layer Security (TLS) library version, or the Hypertext Transfer Protocol (HTTP) User-Agent string. By examining these attributes, the system can better contextualize threat indicators, improving detection accuracy and reducing false positives. The method may also involve dynamically adjusting detection rules or mitigation strategies based on the observed environment characteristics, ensuring more effective threat response. The approach leverages these specific environment characteristics to provide a more granular and adaptive threat detection mechanism. For example, differences in TLS library versions or HTTP User-Agent strings can indicate potential vulnerabilities or attack vectors, allowing the system to tailor its response accordingly. This method enhances traditional threat detection by incorporating real-time environment data, making it more resilient to evolving threats.
3. The method as in claim 1 , wherein the one or more environment characteristics comprise at least one of: an application layer protocol, a flow or congestion control parameter, or a network proxy.
This invention relates to network communication systems, specifically methods for optimizing data transmission by analyzing and adjusting environment characteristics. The problem addressed is the inefficiency in data transfer due to unoptimized network conditions, such as mismatched protocols, suboptimal flow control, or the presence of network proxies that degrade performance. The method involves monitoring and dynamically adapting to one or more environment characteristics that influence data transmission. These characteristics include the application layer protocol used (e.g., HTTP, FTP), flow or congestion control parameters (e.g., TCP window size, packet pacing), and the presence of network proxies (e.g., caching proxies, load balancers). By detecting and adjusting these factors, the system improves throughput, reduces latency, and enhances reliability in data transfers. For example, if a proxy is detected, the method may modify the protocol or adjust flow control settings to bypass or optimize interactions with the proxy. Similarly, if congestion is detected, the method may adjust packet pacing or window sizes to prevent bottlenecks. The system ensures that data transmission aligns with the current network conditions, leading to more efficient and reliable communication. This approach is particularly useful in dynamic environments where network conditions frequently change, such as mobile networks or cloud-based applications.
4. The method as in claim 1 , wherein the traffic generated within a simulation environment comprises encrypted traffic.
The invention relates to network simulation environments, specifically addressing the challenge of accurately modeling real-world network conditions, including encrypted traffic. In network testing and development, it is critical to simulate realistic traffic patterns to evaluate performance, security, and reliability. However, existing simulation environments often fail to replicate encrypted traffic, limiting their ability to test systems under conditions that mirror real-world encrypted communications. The invention provides a method for generating and processing traffic within a simulation environment, where the traffic includes encrypted data. This method involves creating simulated network traffic that mimics encrypted communications, allowing for comprehensive testing of network infrastructure, security protocols, and application performance under realistic conditions. The encrypted traffic is generated using encryption techniques similar to those used in real-world networks, ensuring that the simulation accurately reflects the behavior of encrypted data packets. This enables testing of encryption/decryption processes, network latency, and security vulnerabilities in a controlled environment. The method also supports the simulation of different encryption protocols, such as TLS, SSL, or IPsec, to cover a wide range of use cases. By incorporating encrypted traffic into simulations, the invention enhances the accuracy and effectiveness of network testing, ensuring that systems are evaluated under conditions that closely resemble real-world encrypted communications.
5. The method as in claim 1 , wherein configuring the malware detector to ignore data in the simulation environment data that is associated with the one or more environment characteristics that are not present in the deployment environment characteristics comprises: training, by the device, a second machine learning-based classifier to distinguish between malicious and benign traffic, using the simulation environment data and deployment environment data regarding traffic generated within the network to which the malware detector is to be deployed; and using, by the device, transfer learning, to train the malware detector based on the trained first and second machine learning-based classifiers.
This invention relates to improving malware detection in network environments by adapting machine learning-based classifiers to account for differences between simulation and deployment environments. The problem addressed is that malware detectors trained in simulation environments often perform poorly when deployed in real-world networks due to discrepancies in network traffic patterns, protocols, or other environmental characteristics. The solution involves training a second machine learning classifier specifically to distinguish between malicious and benign traffic using data from both the simulation and deployment environments. Transfer learning is then applied to refine the original malware detector, leveraging insights from the second classifier to improve accuracy in the deployment environment. This approach ensures the detector ignores irrelevant simulation-specific data while retaining effectiveness in real-world conditions. The method enhances adaptability and reliability of malware detection systems across different network environments.
6. The method as in claim 1 , wherein training the malware detector using the simulation environment data comprises: training, by the device, a plurality of traffic classifiers for different combinations of environment characteristics of the simulation environment; and wherein configuring the malware detector to ignore data in the simulation environment data that is associated with the one or more environment characteristics that are not present in the deployment environment characteristics comprises: using, by the device, the traffic classifiers to identify and prevent a portion of the simulation environment data from use as training data for the malware detector.
This invention relates to improving malware detection systems by training them in a simulation environment that mimics real-world deployment conditions. The problem addressed is that malware detectors trained on simulated data often perform poorly when deployed in real environments due to mismatches between simulated and actual conditions. The solution involves training multiple traffic classifiers for different combinations of environment characteristics in the simulation environment. These classifiers are then used to filter out irrelevant or misleading simulation data that does not match the characteristics of the actual deployment environment. By selectively using only the relevant simulation data, the malware detector is trained more effectively, improving its accuracy and reliability in real-world scenarios. The system dynamically adjusts the training process to exclude data associated with environment characteristics not present in the deployment environment, ensuring the detector is optimized for its specific operational context. This approach enhances the adaptability and performance of malware detection systems in diverse real-world settings.
7. The method as in claim 1 , wherein configuring the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics comprises: configuring, by the device, the malware detector to identify the one or more environment characteristics using one or more rules or traffic classifiers.
This invention relates to improving malware detection in simulated environments by filtering out irrelevant data that does not match the characteristics of the actual deployment environment. The problem addressed is that malware detectors trained or tested in simulation environments may produce false positives or negatives when deployed in real-world settings due to discrepancies between simulated and actual environment conditions. The solution involves configuring a malware detector to ignore data in the simulation environment that is associated with environment characteristics not present in the deployment environment. This is achieved by identifying such characteristics using predefined rules or traffic classifiers. The rules or classifiers help distinguish between simulation-specific data and real-world relevant data, ensuring the malware detector focuses only on pertinent information. This approach enhances the accuracy and reliability of malware detection by reducing noise from simulated conditions that do not reflect the actual deployment environment. The method ensures that the detector operates effectively in real-world scenarios by filtering out irrelevant simulation artifacts.
8. The method as in claim 1 , wherein the malware detector ignores the data associated with the simulation environment by filtering out statistical patterns or artifacts that are generated by the simulation environment and not by the malware.
A method for improving malware detection in simulated environments involves filtering out simulation-specific artifacts to enhance detection accuracy. The core technique identifies and removes statistical patterns or data artifacts generated by the simulation environment itself, rather than by actual malware, to prevent false positives. This is particularly useful in virtualized or emulated environments where simulation-related noise can obscure genuine malicious activity. The method first collects data from the simulated environment, then analyzes it to distinguish between simulation-generated patterns and potential malware indicators. By filtering out the former, the malware detector focuses only on relevant data, improving detection reliability. The approach may involve statistical analysis, pattern recognition, or machine learning to differentiate between simulation artifacts and true malware behavior. This ensures that security tools operating in simulated environments can accurately identify real threats without being misled by environmental noise. The technique is applicable to various simulation platforms, including virtual machines, emulators, and sandboxed environments, where distinguishing between simulated and malicious activity is critical for effective cybersecurity.
9. An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to: obtain simulation environment data regarding traffic generated within a simulation environment in which malware is executed; train a malware detector using the simulation environment data; obtain deployment environment characteristics of a network to which the malware detector is to be deployed; train a first machine learning-based classifier to distinguish between traffic in the simulation environment in which malware is executed and traffic in a deployment environment in which the malware detector is to be deployed; and configure the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics using the trained first machine learning-based classifier.
This invention relates to improving malware detection systems by adapting simulation-based training to real-world deployment environments. The problem addressed is the mismatch between simulated malware traffic patterns and actual network traffic, which reduces the effectiveness of malware detectors trained in simulation environments. The apparatus includes network interfaces for communication, a processor, and memory storing a process. The process obtains simulation environment data from a malware execution simulation, then trains a malware detector using this data. It also gathers deployment environment characteristics of the target network where the detector will operate. A machine learning-based classifier is trained to differentiate between simulation and real-world traffic patterns. The malware detector is then configured to filter out simulation data that does not align with the deployment environment's characteristics, ensuring the detector focuses on relevant traffic patterns. This approach enhances detection accuracy by reducing false positives and negatives caused by environment discrepancies. The system dynamically adapts to the specific conditions of the deployment network, improving malware detection performance in real-world scenarios.
10. The apparatus as in claim 9 , wherein the one or more environment characteristics comprise at least one of: an endpoint operating system, a Transport Layer Security (TLS) library, or a Hypertext Transfer Protocol (HTTP) User-Agent.
This invention relates to a system for analyzing and managing network security by evaluating environment characteristics of endpoints in a network. The system identifies and assesses specific attributes of endpoints to determine potential security risks or vulnerabilities. The environment characteristics include the operating system running on the endpoint, the Transport Layer Security (TLS) library used for secure communications, and the Hypertext Transfer Protocol (HTTP) User-Agent string, which provides information about the browser or application making requests. By analyzing these characteristics, the system can detect outdated or insecure configurations that may expose the endpoint to attacks. The system may also compare these characteristics against known vulnerabilities or security standards to recommend updates or mitigations. This approach helps organizations maintain secure network environments by ensuring endpoints adhere to best practices and are protected against common threats. The system can be integrated into existing security frameworks to enhance threat detection and response capabilities.
11. The apparatus as in claim 9 , wherein the one or more environment characteristics comprise at least one of: an application layer protocol, a flow or congestion control parameter, or a network proxy.
This invention relates to network communication systems, specifically addressing challenges in optimizing data transmission based on environmental factors. The apparatus includes a network interface for receiving data packets and a processor configured to analyze one or more environment characteristics to determine optimal transmission parameters. These characteristics include application layer protocols, flow or congestion control parameters, and network proxies. The processor adjusts transmission settings such as packet size, timing, or routing based on these factors to improve efficiency, reduce latency, or enhance reliability. The system may also monitor network conditions in real-time to dynamically adapt to changes. By considering multiple environmental variables, the apparatus ensures better performance in diverse network scenarios, including high-latency or congested environments. The invention aims to provide a flexible and adaptive solution for optimizing data transfer in varying network conditions.
12. The apparatus as in claim 9 , wherein the traffic generated within a simulation environment comprises encrypted traffic.
The invention relates to network simulation systems designed to test and validate network security and performance. The core problem addressed is the need for realistic simulation environments that accurately replicate real-world network conditions, including encrypted traffic, to effectively evaluate security protocols, intrusion detection systems, and network performance under realistic scenarios. The apparatus includes a simulation environment capable of generating network traffic that mimics real-world conditions. This traffic can be encrypted, allowing for the testing of security mechanisms such as encryption protocols, decryption processes, and secure communication channels. The system may also include components for monitoring, analyzing, and modifying the simulated traffic to assess how network devices and security systems respond to various encrypted and unencrypted traffic patterns. By incorporating encrypted traffic, the simulation provides a more accurate representation of modern network environments, where encryption is widely used for security and privacy. The apparatus may further include tools for configuring the simulation parameters, such as traffic volume, encryption types, and network topologies, to create customized test scenarios. This flexibility ensures that the simulation can be tailored to specific use cases, such as testing firewall rules, VPN performance, or endpoint security measures. The system may also log and report simulation results, enabling detailed analysis of how network security systems handle encrypted traffic, identify vulnerabilities, and optimize performance.
13. The apparatus as in claim 9 , wherein the apparatus configures the malware detector to ignore data in the simulation environment data that is associated with the one or more environment characteristics that are not present in the deployment environment characteristics by: training a second machine learning-based classifier to distinguish between malicious and benign traffic, using the simulation environment data and deployment environment data regarding traffic generated within the network to which the malware detector is to be deployed; and using transfer learning, to train the malware detector based on the trained first and second machine learning-based classifiers.
This invention relates to improving malware detection in network environments by adapting a malware detector to differences between a simulation environment and a real deployment environment. The problem addressed is that malware detectors trained in simulation environments often perform poorly when deployed in real networks due to discrepancies in network characteristics, traffic patterns, or other environmental factors. The solution involves a system that configures a malware detector to ignore irrelevant simulation data by leveraging transfer learning and multiple machine learning classifiers. The system trains a first classifier to detect malware using simulation environment data, which includes network traffic and other characteristics specific to the simulated environment. A second classifier is trained to distinguish between malicious and benign traffic using both simulation and deployment environment data, focusing on traffic generated within the target network. Transfer learning is then applied to refine the malware detector by combining insights from both classifiers, effectively filtering out simulation-specific data that does not apply to the deployment environment. This approach enhances the detector's accuracy and reliability in real-world scenarios by aligning its training with the actual conditions it will encounter.
14. The apparatus as in claim 9 , wherein the apparatus trains the malware detector using the simulation environment data by: training a plurality of traffic classifiers for different combinations of environment characteristics of the simulation environment; and wherein configuring the malware detector to ignore data in the simulation environment data that is associated with the one or more environment characteristics that are not present in the deployment environment characteristics comprises: using the traffic classifiers to identify and prevent a portion of the simulation environment data from use as training data for the malware detector.
This invention relates to cybersecurity, specifically improving malware detection systems by training them in a simulation environment that mimics real-world deployment conditions. The problem addressed is that traditional malware detectors trained on simulated data often perform poorly when deployed in real environments due to mismatches between simulated and actual conditions. The solution involves an apparatus that trains a malware detector using simulation environment data while accounting for differences between the simulation and deployment environments. The apparatus trains multiple traffic classifiers, each optimized for different combinations of environment characteristics (e.g., network traffic patterns, system configurations). During training, the apparatus uses these classifiers to filter out simulation data that does not match the deployment environment's characteristics. This ensures the malware detector is trained only on relevant data, improving accuracy in real-world scenarios. The system dynamically adjusts training based on the deployment environment's specific conditions, reducing false positives and negatives. This approach enhances the reliability of malware detection by aligning simulated training with real-world operational constraints.
15. The apparatus as in claim 9 , wherein the apparatus configures the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics by: configuring the malware detector to identify the one or more environment characteristics using one or more rules or traffic classifiers.
This invention relates to malware detection systems that operate in simulation environments to test and validate malware detection logic before deployment. The problem addressed is ensuring that malware detectors trained or tested in simulation environments accurately perform in real-world deployment environments, where environmental differences (e.g., network traffic patterns, system configurations, or user behavior) can lead to false positives or negatives. The solution involves configuring the malware detector to filter out simulation-specific data that does not correlate with the deployment environment. This is achieved by identifying and ignoring data associated with environment characteristics absent in the deployment environment. The system uses predefined rules or traffic classifiers to detect these characteristics, ensuring the malware detector focuses only on relevant data. The rules or classifiers may be based on patterns, signatures, or behavioral traits unique to the simulation environment. This approach improves the reliability of malware detection by reducing noise and irrelevant data, making the detector more accurate when deployed in real-world scenarios. The invention is particularly useful in cybersecurity applications where simulation testing is critical for validating detection algorithms before live deployment.
16. The apparatus as in claim 9 , wherein the process when executed is further configured to: execute the malware in the simulation environment within a virtual machine.
A system and method for analyzing malware in a secure simulation environment. The technology addresses the challenge of safely examining malicious software without risking infection of the host system or network. The apparatus includes a simulation environment that replicates a target system's operating conditions, allowing malware to be executed and monitored in isolation. The process involves running the malware within a virtual machine to further isolate the execution from the host system, preventing any potential escape or lateral movement. The simulation environment captures and analyzes the malware's behavior, including system calls, network activity, and file modifications, to determine its functionality and impact. This approach enables security researchers and analysts to study malware without exposing real systems to risk, improving threat intelligence and defense strategies. The use of a virtual machine adds an additional layer of security by ensuring the malware cannot directly interact with the host system's hardware or other virtual machines. The system may also include features for automated analysis, such as signature generation, behavior profiling, and threat classification, to streamline the malware analysis process. This technology is particularly useful in cybersecurity, incident response, and threat intelligence applications.
17. The apparatus as in claim 9 , wherein the process when executed is further configured to: deploy the malware detector to the network to which the malware detector is to be deployed.
A system for deploying a malware detector within a network environment addresses the challenge of efficiently and securely distributing malware detection capabilities across networked systems. The system includes a malware detector configured to analyze network traffic, identify malicious patterns, and mitigate threats in real-time. The deployment process involves automatically distributing the malware detector to the target network, ensuring seamless integration with existing infrastructure. The system may also include a central management module that oversees deployment, monitors performance, and updates the malware detector as needed. The deployment process ensures that the malware detector is properly configured and operational upon installation, minimizing downtime and reducing the risk of security vulnerabilities during deployment. The system may further include mechanisms for verifying the integrity and authenticity of the deployed malware detector to prevent tampering or unauthorized modifications. This approach enhances network security by providing a scalable and automated solution for deploying advanced malware detection capabilities across diverse network environments.
18. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: obtaining, by the device, simulation environment data regarding traffic generated within a simulation environment in which malware is executed; training, by the device, a malware detector using the simulation environment data; obtaining, by the device, deployment environment characteristics of a network to which the malware detector is to be deployed; training, by the device, a first machine learning-based classifier to distinguish between traffic in the simulation environment in which malware is executed and traffic in a deployment environment in which the malware detector is to be deployed; and configuring, by the device, the malware detector to ignore data in the simulation environment data that is associated with one or more environment characteristics that are not present in the deployment environment characteristics using the trained first machine learning-based classifier.
This invention relates to improving malware detection systems by adapting them to different network environments. The problem addressed is that malware detectors trained in simulation environments often produce false positives or negatives when deployed in real-world networks due to differences in traffic patterns. The solution involves a computer-readable medium storing instructions for a process that enhances malware detection accuracy by customizing the detector to the specific deployment environment. The process begins by obtaining simulation environment data containing traffic generated during malware execution in a simulated environment. A malware detector is then trained using this data. Next, deployment environment characteristics of the target network are obtained, such as network topology, traffic patterns, or other distinguishing features. A machine learning-based classifier is trained to differentiate between traffic in the simulation environment and the deployment environment. Finally, the malware detector is configured to ignore simulation data associated with environment characteristics not present in the deployment environment, ensuring the detector focuses on relevant traffic patterns. This approach reduces false detections by aligning the detector's training data with the actual deployment conditions.
19. The computer-readable medium as in claim 18 , wherein the one or more environment characteristics comprise at least one of: an endpoint operating system, a Transport Layer Security (TLS) library, a Hypertext Transfer Protocol (HTTP) User-Agent, an application layer protocol, a flow or congestion control parameter, or a network proxy.
This invention relates to network security and performance optimization, specifically addressing the challenge of dynamically adapting network communications based on environmental characteristics to improve security, efficiency, and compatibility. The system involves analyzing one or more environment characteristics to determine optimal configurations for network interactions. These characteristics include the endpoint operating system, Transport Layer Security (TLS) library, HTTP User-Agent, application layer protocols, flow or congestion control parameters, and network proxies. By evaluating these factors, the system can adjust settings such as encryption protocols, data transmission methods, or proxy configurations to enhance security, reduce latency, or ensure compatibility with different network environments. The invention ensures that network communications are optimized for the specific conditions of the endpoint and network infrastructure, improving overall performance and security. This approach is particularly useful in heterogeneous environments where devices and networks may have varying capabilities and requirements.
20. The computer-readable medium as in claim 18 , wherein configuring the malware detector to ignore data in the simulation environment data that is associated with the one or more environment characteristics that are not present in the deployment environment characteristics comprises: training, by the device, a second machine learning-based classifier to distinguish between malicious and benign traffic, using the simulation environment data and deployment environment data regarding traffic generated within the network to which the malware detector is to be deployed; and using, by the device, transfer learning, to train the malware detector based on the trained first and second machine learning-based classifiers.
This invention relates to improving malware detection in network environments by adapting machine learning models to differences between simulation and deployment environments. The problem addressed is that malware detectors trained in simulation environments often perform poorly when deployed in real-world networks due to discrepancies in network traffic patterns, protocols, or other environmental characteristics. The solution involves training a first machine learning classifier on simulation environment data and a second classifier on deployment environment data. The system then uses transfer learning to refine the malware detector, enabling it to ignore irrelevant simulation-specific data while retaining useful patterns. This approach ensures the detector accurately distinguishes between malicious and benign traffic in the actual deployment environment. The method leverages both simulated and real-world data to enhance detection accuracy and reliability, reducing false positives and negatives caused by environmental mismatches. The invention is particularly useful in cybersecurity applications where accurate threat detection is critical.
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December 1, 2020
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